The “immersed boundary method (IBM)” is considered to be the most efficacious and versatile technique to solve flow problems associated with intricate geometries. The first part of this review examines recent advancements in IBM, essential for the simulation of “fluid–structure interactions (FSIs)” in sophisticated systems. This review highlights significant developments in turbulence modeling, adaptive mesh refinement, and complex geometric simulations, demonstrating IB methods’ capacity to seamlessly integrate arbitrary geometries into structured computational grids while preserving computational efficiency. Various IB techniques are analyzed for enforcing boundary conditions on dynamic immersed boundaries, with notable breakthroughs in managing velocity discontinuities, spurious oscillations, and large-scale deformations. Recent findings illustrate the versatility of IB methods, with applications encompassing biological fluid dynamics, turbulent multiphase flows, and cavitating flows. These innovations not only enhance computational performance but also address evolving challenges across engineering and scientific fields, establishing IB methods as a robust tool for resolving complex, multidisciplinary problems with high accuracy and efficiency.

Detecting glucose levels is crucial for diabetes patients as it enables timely and effective management, preventing complications and promoting overall health. In this endeavor, we have designed a novel, affordable point-of-care diagnostic device utilizing microfluidic principles, a smartphone camera, and established laboratory colorimetric methods for accurate glucose estimation. Our proposed microfluidic device comprises layers of adhesive poly-vinyl films stacked on a poly methyl methacrylate (PMMA) base sheet, with micro-channel contours precision-cut using a cutting printer. Employing the gold standard glucose-oxidase/peroxidase reaction on this microfluidic platform, we achieve enzymatic glucose determination. The resulting colored complex, formed by phenol and 4-aminoantipyrine in the presence of hydrogen peroxide generated during glucose oxidation, is captured at various glucose concentrations using a smartphone camera. Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 95% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms. Furthermore, the classifier exhibits outstanding precision, recall, and F1 score of 94%, 93%, and 93%, respectively, as validated through our study, showcasing its exceptional predictive capability. Next, a user-friendly smartphone application named “GLUCOLENS AI” was developed to capture images, perform image processing, and communicate with cloud server containing the CNN classifier. The developed CNN model can be successfully used as a pre-trained model for future glucose concentration predictions.

This article discusses contemporary strategies to deal with immersed boundary (IB) frameworks useful for analyzing flow–structure interaction in complex settings. It focuses on immense advancements in various fields: biology, oscillation of structures due to fluid flow, deformable materials, thermal processes, settling particles, multiphase systems, and sound propagation. The discussion also involves a review of techniques addressing moving boundary conditions at complex interfaces. Evaluating practical examples and theoretical challenges that have been addressed by these frameworks are another focus of the article. Important results highlight the integration of IB methods with adaptive mesh refinement and high-order accuracy techniques, which enormously improve computational efficiency and precision in modeling complex solid–fluid interactions. The article also describes the evolution of IB methodologies in tackling problems of energy harvesting, bio-inspiration propulsion, and thermal-fluid coupling, which extends IB methodologies broadly in many scientific and industrial areas. More importantly, by bringing together different insights and paradigms from across disciplines, the study highlights the emerging trends in IB methodologies towards solving some of the most intricate challenges within the technical and scientific domains.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by impairments in attention, impulsivity, and executive function. Current diagnostic approaches rely primarily on subjective behavioral assessments, highlighting the need for objective and interpretable neurophysiological biomarkers. This study proposes a Multi-Objective Rank Scored–SHapley Additive exPlanations (MRS-SHAP) framework for EEG-based ADHD analysis, integrating explainability, temporal stability, and performance-aware feature selection within a unified pipeline. A publicly available dataset comprising 121 children (61 ADHD, 60 controls; age 7–12 years), recorded using a 19-channel EEG system, was utilized. A total of 998 features were extracted across spectral, nonlinear, and connectivity domains and dataset does not include subtype-specific ADHD labels; therefore, the analysis focuses on general ADHD classification. The proposed approach combines SHAP-based feature importance, Dynamic Time Warping (DTW)-based temporal stability, and cross-validated performance weighting to derive a compact, robust, and explainable feature subset. All preprocessing, resampling (SMOTE-ENN), feature selection, and model training steps were implemented within a strictly nested, leakage-free cross-validation framework to ensure unbiased evaluation. Under segment-level evaluation, the CatBoost classifier achieved high performance (Accuracy = 98.40%, F1-score = 98.40%), representing an upper-bound estimate. However, subject-level validation using GroupKFold yielded more realistic performance (Accuracy ≈ 79.5%, AUC ≈ 0.86), while evaluation on a held-out test set (~ 20% of subjects) produced a more conservative estimate (Accuracy ≈ 65.7%, AUC ≈ 0.73). This performance gap highlights the impact of inter-subject variability and emphasizes the importance of strict subject-level validation in EEG-based modeling. Comparative analysis demonstrates that the proposed feature selection approach outperforms conventional methods such as PCA, ANOVA, and Recursive Feature Elimination (RFE). SHAP-based interpretability identifies beta-band activity, EEG microstate dynamics, and fronto-central connectivity as key discriminative features, consistent with established neurophysiological evidence of ADHD-related network dysfunction. Ablation and sensitivity analyses further confirm the complementary contributions of feature importance and temporal stability, with minimal dependence on synthetic resampling. These findings indicate that integrating explainability and temporal stability enhances the robustness and interpretability of EEG-based feature selection. However, the results should be interpreted as methodological validation rather than clinical evidence, as the study is limited to a single dataset and may be influenced by confounding factors such as medication exposure. Future work will focus on external validation across independent cohorts and clinically controlled settings to assess generalizability and translational relevance.

Seizure detection from scalp EEG recordings requires features capable of capturing the rapidly evolving patterns of cerebral activity. We present a strictly patient-independent approach that investigates 1-second EEG windows using nested cross-validation to prevent data leakage within the paediatric CHB-MIT dataset. In this approach, we combine features extracted from temporal, spectral, wavelet, and spatial (CSP) domains. Feature selection, spatial filtering, and hyperparameter optimization are performed only within training folds to avoid leakage. A compact feature set of just 15 features enables traditional classifiers to achieve remarkable performance: a K-Nearest Neighbours classifier achieves 90.6% accuracy, whereas a Random Forest achieves 90.5% accuracy with an AUC of 0.927. The proposed system is interpretable, highly computationally efficient, and suitable for near real-time clinical monitoring.